Identification

Author

Trock SC, Burke SA, Cox NJ

Title

Development of an influenza virologic risk assessment tool

Year

2012

Publication type

Article

Journal

Avian diseases

Created

2014-04-08 22:17:32+00:00

Modified

2016-07-13 21:32:28.539840+00:00

Details

Volume

56

Number

4

Access

Language

English

URL http://www.offlu.net/fileadmin/home/en/human-animal-interface/pdf/CDC_IRAT_paper.pdf
DOI

http://dx.doi.org/10.1637/10204-041412-ResNote.1

Accessed

2016-07-13

Extended information

Abstract

Influenza pandemics pose a continuous risk to human and animal health and may engender food security issues worldwide. As novel influenza A virus infections in humans are identified, pandemic preparedness strategies necessarily involve decisions regarding which viruses should be included for further studies and mitigation efforts. Resource and time limitations dictate that viruses determined to pose the greatest risk to public or animal health should be selected for further research to fill information gaps and, potentially, for development of vaccine candidates that could be put in libraries, manufactured and stockpiled, or even administered to protect susceptible populations of animals or people. A need exists to apply an objective, science-based risk assessment to the process of evaluating influenza viruses. During the past year, the Centers for Disease Control and Prevention began developing a tool to evaluate influenza A viruses that are not circulating in the human population but pose a pandemic risk. The objective is to offer a standardized set of considerations to be applied when evaluating prepandemic viruses. The tool under consideration is a simple, additive model, based on multiattribute decision analysis. The model includes elements that address the properties of the virus itself and population attributes, considers both veterinary and human findings, and integrates both laboratory and field observations. Additionally, each element is assigned a weight such that all elements are not considered of equal importance within the model.